Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT

Assessment of pulmonary vascular anatomy: comparing augmented reality by holograms versus standard CT images/reconstructions using surgical findings as reference standard

BACKGROUND

We compared computed tomography (CT) images and holograms (HG) to assess the number of arteries of the lung lobes undergoing lobectomy and assessed easiness in interpretation by radiologists and thoracic surgeons with both techniques.

METHODS

Patients scheduled for lobectomy for lung cancer were prospectively included and underwent CT for staging. A patient-specific three-dimensional model was generated and visualized in an augmented reality setting. One radiologist and one thoracic surgeon evaluated CT images and holograms to count lobar arteries, having as reference standard the number of arteries recorded at surgery. The easiness of vessel identification was graded according to a Likert scale. Wilcoxon signed-rank test and κ statistics were used.

RESULTS

Fifty-two patients were prospectively included. The two doctors detected the same number of arteries in 44/52 images (85%) and in 51/52 holograms (98%). The mean difference between the number of artery branches detected by surgery and CT images was 0.31 ± 0.98, whereas it was 0.09 ± 0.37 between surgery and HGs (p = 0.433). In particular, the mean difference in the number of arteries detected in the upper lobes was 0.67 ± 1.08 between surgery and CT images and 0.17 ± 0.46 between surgery and holograms (p = 0.029). Both radiologist and surgeon showed a higher agreement for holograms (κ = 0.99) than for CT (κ = 0.81) and found holograms easier to evaluate than CTs (p < 0.001).

CONCLUSIONS

Augmented reality by holograms is an effective tool for preoperative vascular anatomy assessment of lungs, especially when evaluating the upper lobes, more prone to anatomical variations.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT04227444 RELEVANCE STATEMENT: Preoperative evaluation of the lung lobe arteries through augmented reality may help the thoracic surgeons to carefully plan a lobectomy, thus contributing to optimize patients' outcomes.

KEY POINTS

• Preoperative assessment of the lung arteries may help surgical planning. • Lung artery detection by augmented reality was more accurate than that by CT images, particularly for the upper lobes. • The assessment of the lung arterial vessels was easier by using holograms than CT images.

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis

Purpose

We aim to determine the combination of X-ray spectrum and detector scintillator thickness that maximizes the detectability of microcalcification clusters in dedicated cone-beam breast CT.

Approach

A cascaded linear system analysis was implemented in the spatial frequency domain and was used to determine the detectability index using numerical observers for the imaging task of detecting a microcalcification cluster with 0.17 mm diameter calcium carbonate spheres. The analysis considered a thallium-doped cesium iodide scintillator coupled to a complementary metal-oxide semiconductor detector and an analytical filtered-back-projection reconstruction algorithm. Independent system parameters considered were the scintillator thickness, applied X-ray tube voltage, and X-ray beam filtration. The combination of these parameters that maximized the detectability index was considered optimal.

Results

Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6 mm thick scintillator, 70 kV, and 0.25 to 0.4 mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5 mGy.

Conclusion

Using parallel cascade systems' analysis, the combination of parameters that could maximize the detection of microcalcifications was identified. The analysis indicates that a harder beam than that used in current practice may be beneficial for the task of detecting microcalcifications at an MGD suitable for breast cancer screening.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging

BACKGROUND

The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization.

PURPOSE

To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction.

METHODS

A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49 kV with 1.5 mm aluminum filter and at 65 kV and 0.257 mm copper filter. A 10 mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5 mm iodine disk detection task at 49, 55, 60, and 65 kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4 mm.

RESULTS

At 49 kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min = 3.7%; max = 8.5%). At 65 kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min = 11.4%; max = 16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2 = 0.993) and 19.14 µGy/mA (R2 = 0.996) at 49 and 65 kV, respectively. For iodine detection, a 0.25 mm-thick copper filter at 65 kV was found optimal, outperforming the default technique by 90%.

CONCLUSION

The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

Objective. To compare the dosimetric performance of three cone-beam breast computed tomography (BCT) scanners, using real-time Monte Carlo-based dose estimates obtained with the virtual clinical trials (VCT)-BREAST graphical processing unit (GPU)-accelerated platform dedicated to VCT in breast imaging. Approach. A GPU-based Monte Carlo (MC) code was developed for replicatingin silicothe geometric, x-ray spectra and detector setups adopted, respectively, in two research scanners and one commercial BCT scanner, adopting 80 kV, 60 kV and 49 kV tube voltage, respectively. Our cohort of virtual breasts included 16 anthropomorphic voxelized breast phantoms from a publicly available dataset. For each virtual patient, we simulated exams on the three scanners, up to a nominal simulated mean glandular dose of 5 mGy (primary photons launched, in the order of 1011-1012per scan). Simulated 3D dose maps (recorded for skin, adipose and glandular tissues) were compared for the same phantom, on the three scanners. MC simulations were implemented on a single NVIDIA GeForce RTX 3090 graphics card.Main results.Using the spread of the dose distribution as a figure of merit, we showed that, in the investigated phantoms, the glandular dose is more uniform within less dense breasts, and it is more uniformly distributed for scans at 80 kV and 60 kV, than at 49 kV. A realistic virtual study of each breast phantom was completed in about 3.0 h with less than 1% statistical uncertainty, with 109primary photons processed in 3.6 s computing time.Significance. We reported the first dosimetric study of the VCT-BREAST platform, a fast MC simulation tool for real-time virtual dosimetry and imaging trials in BCT, investigating the dose delivery performance of three clinical BCT scanners. This tool can be adopted to investigate also the effects on the 3D dose distribution produced by changes in the geometrical and spectrum characteristics of a cone-beam BCT scanner.

Discrimination tasks in simulated low-dose CT noise

BACKGROUND

This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions.

PURPOSE

The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions.

MATERIALS AND METHODS

The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain.

RESULTS

Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of "Unapodized" classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters.

CONCLUSIONS

These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

Cone-beam breast CT using an offset detector: effect of detector offset and image reconstruction algorithm

Objective.A dedicated cone-beam breast computed tomography (BCT) using a high-resolution, low-noise detector operating in offset-detector geometry has been developed. This study investigates the effects of varying detector offsets and image reconstruction algorithms to determine the appropriate combination of detector offset and reconstruction algorithm.Approach.Projection datasets (300 projections in 360°) of 30 breasts containing calcified lesions that were acquired using a prototype cone-beam BCT system comprising a 40 × 30 cm flat-panel detector with 1024 × 768 detector pixels were reconstructed using Feldkamp-Davis-Kress (FDK) algorithm and served as the reference. The projection datasets were retrospectively truncated to emulate cone-beam datasets with sinograms of 768×768 and 640×768 detector pixels, corresponding to 5 cm and 7.5 cm lateral offsets, respectively. These datasets were reconstructed using the FDK algorithm with appropriate weights and an ASD-POCS-based Fast, total variation-Regularized, Iterative, Statistical reconstruction Technique (FRIST), resulting in a total of 4 offset-detector reconstructions (2 detector offsets × 2 reconstruction methods). Signal difference-to-noise ratio (SDNR), variance, and full-width at half-maximum (FWHM) of calcifications in two orthogonal directions were determined from all reconstructions. All quantitative measurements were performed on images in units of linear attenuation coefficient (1/cm).Results.The FWHM of calcifications did not differ (P > 0.262) among reconstruction algorithms and detector formats, implying comparable spatial resolution. For a chosen detector offset, the FRIST algorithm outperformed FDK in terms of variance and SDNR (P < 0.0001). For a given reconstruction method, the 5 cm offset provided better results.Significance.This study indicates the feasibility of using the compressed sensing-based, FRIST algorithm to reconstruct sinograms from offset-detectors. Among the reconstruction methods and detector offsets studied, FRIST reconstructions corresponding to a 30 cm × 30 cm with 5 cm lateral offset, achieved the best performance. A clinical prototype using such an offset geometry has been developed and installed for clinical trials.

Comparisons of glandular breast dose between digital mammography, tomosynthesis and breast CT based on anthropomorphic patient-derived breast phantoms

PURPOSE

To evaluate the bias to the mean glandular dose (MGD) estimates introduced by the homogeneous breast models in digital breast tomosynthesis (DBT) and to have an insight into the glandular dose distributions in 2D (digital mammography, DM) and 3D (DBT and breast dedicated CT, BCT) x-ray breast imaging by employing breast models with realistic glandular tissue distribution and organ silhouette.

METHODS

A Monte Carlo software for DM, DBT and BCT simulations was adopted for the evaluation of glandular dose distribution in 60 computational anthropomorphic phantoms. These computational phantoms were derived from 3D breast images acquired via a clinical BCT scanner.

RESULTS

g·c·s·T conversion coefficients based on homogeneous breast model led to a MGD overestimate of 18% in DBT when compared to MGD estimated via anthropomorphic phantoms; this overestimate increased up to 21% for recently computed DgN_DBT conversion coefficients. The standard deviation of the glandular dose distribution in BCT resulted 60% lower than in DM and 55% lower than in DBT. The glandular dose peak - evaluated as the average value over the 5% of the gland receiving the highest dose - is 2.8 times the MGD in DM, this factor reducing to 2.6 and 1.6 in DBT and BCT, respectively.

CONCLUSIONS

Conventional conversion coefficients for MGD estimates based on homogeneous breast models overestimate MGD by 18%, when compared to MGD estimated via anthropomorphic phantoms. The ratio between the peak glandular dose and the MGD is 2.8 in DM. This ratio is 8% and 75% higher than in DBT and BCT, respectively.

[A Method to Obtain a Sheet-like X-ray Phantom Made from Iodine Contrast Agent]

PURPOSE

A sheet-like X-ray phantom on which thin Iodine is uniformly coated was developed to facilitate the handling of iodined objects used in any X-ray imaging studies.

METHODS

The most recommendable protocol as follows: (1) prepare undiluted 240 mg/ml Iohexol-based contrast agent and drop around 1.6 g on a horizontal surface. (2) infiltrate the agent into a membrane filter (47 mm in diameter) from the secondary side. (3) one minute later, the excess liquid components should be removed by a softy absorbent paper, and the infiltrated membrane filter should be left until substantially dried.

RESULT

The dried membrane filter can be utilized as a sheet-like X-ray phantom on which the iodine of around 2.45 mg is almost uniformly distributed per 1 cm². However, since the iodine density is slightly higher on the periphery part of the sheet, less than 80% area of the entire sheet is recommended to be used from a viewpoint of the spatial uniformity.

CONCLUSION

In the practical experiments, the X-ray attenuation factor can be controlled by changing the stacking number of the sheet, and the spatial size and form can also be designed by cutting the sheet. This capability is expected to improve the efficiency of any X-ray experiments and quality control works that requires iodined materials.

Dose reduction potential in diagnostic single energy CT through patient-specific prefilters and a wider range of tube voltages

PURPOSE

Various studies have demonstrated that additional prefilters and/or reduced tube voltages have the potential to significantly increase the contrast-to-noise ratios at unit dose (CNRDs) and thereby to significantly reduce patient dose in clinical CT. An exhaustive analysis, accounting for a wide range of filter thicknesses and a wide range of tube voltages extending beyond the 70 to 150 kV range of today's CT systems, including their specific choice depending on the patient size, is, however, missing. Therefore, this work analyzes the dose reduction potential for patient-specific selectable prefilters combined with a wider range of tube voltages. We do so for soft tissue and iodine contrast in single energy CT. The findings may be helpful to guide further developments of x-ray tubes and automatic filter changers.

METHODS

CT acquisitions were simulated for different patient sizes (semianthropomorphic phantoms for child, adult, and obese patients), tube voltages (35-150 kV), prefilter materials (tin and copper), and prefilter thicknesses (up to 5 mm). For each acquisition soft tissue and iodine CNRDs were determined. Dose was calculated using Monte Carlo simulations of a computed tomography dose index (CTDI) phantom. CNRD values of acquisitions with different parameters were used to evaluate dose reduction.

RESULTS

Dose reduction through patient-specific prefilters depends on patient size and available tube current among others. With an available tube current time product of 1000 mAs dose reductions of 17% for the child, 32% for the adult and 29% for the obese phantom were achieved for soft tissue contrast. For iodine contrast dose reductions were 57%, 49%, and 39% for child, adult, and obese phantoms, respectively. Here, a tube voltage range extended to lower kV is important.

CONCLUSIONS

Substantial dose reduction can be achieved by utilizing patient-specific prefilters. Tube voltages lower than 70 kV are beneficial for dose reduction with iodine contrast, especially for small patients. The optimal implementation of patient-specific prefilters benefits from higher tube power. Tin prefilters should be available in 0.1 mm steps or lower, copper prefilter in 0.3 mm steps or lower. At least 10 different prefilter thicknesses should be used to cover the dose optima of all investigated patient sizes and contrast mechanisms. In many cases it would be advantageous to adapt the prefilter thickness rather than the tube current to the patient size, that is, to always use the maximum available tube current and to control the exposure by adjusting the thickness of the prefilter.

High-resolution μ CT imaging for characterizing microcalcification detection performance in breast CT

Purpose: To demonstrate the utility of high-resolution micro-computed tomography ( μ CT ) for determining ground-truth size and shape properties of calcium grains for evaluation of detection performance in breast CT (bCT). Approach: Calcium carbonate grains ( ∼ 200    μ m ) were suspended in 1% agar solution to emulate microcalcifications ( μ Calcs ) within a fibroglandular tissue background. Ground-truth imaging was performed on a commercial μ CT scanner and was used for assessing calcium-grain size and shape, and for generating μ Calc signal profiles. Calcium grains were placed within a realistic breast-shaped phantom and imaged on a prototype bCT system at 3- and 6-mGy mean glandular dose (MGD) levels, and the non-prewhitening detectability was assessed. Additionally, the μ CT -derived signal profiles were used in conjunction with the bCT system characterization (MTF and NPS) to obtain predictions of bCT detectability. Results: Estimated detectability of the calcium grains on the bCT system ranged from 2.5 to 10.6 for 3 mGy and from 3.8 to 15.3 for 6 mGy with large fractions of the grains meeting the Rose criterion for visibility. Segmentation of μ CT images based on morphological operations produced accurate results in terms of segmentation boundaries and segmented region size. A regression model linking bCT detectability to μ Calc parameters indicated significant effects of μ Calc size and vertical position within the breast phantom. Detectability using μ CT -derived detection templates and bCT statistical properties (MTF and NPS) were in good correspondence with those measured directly from bCT ( R 2 > 0.88 ). Conclusions: Parameters derived from μ CT ground-truth data were shown to produce useful characterizations of detectability when compared to estimates derived directly from bCT. Signal profiles derived from μ CT imaging can be used in conjunction with measured or hypothesized statistical properties to evaluate the performance of a system, or system component, that may not currently be available.

Quantification of Airway Dimensions using a High-Resolution CT Scanner: A Phantom Study

PURPOSE

Small airways with inner diameters less than 2 mm are sites of major airflow limitations in patients with chronic obstructive pulmonary disease (COPD) and asthma. The purpose of this study is to investigate the limitations for accurate assessment of small airway dimensions using both high-resolution CT (HRCT) and conventional normal-resolution CT at low dose levels.

METHODS

To model the normal human airways from the 3^rd to 20^th generations, a cylindrical polyurethane phantom with 14 airway tubes of inner diameters (ID) ranging from 0.3 to 3.4 mm and wall thicknesses (WT) ranging from 0.15 to 1.6 mm was placed within an Anthropomorphic QRM-Thorax phantom. The Aquilion Precision (Canon Medical Systems Corporation) HRCT scanner was used to acquire images at 80, 100 and 120 kV using high resolution mode (HR, 0.25 mm x 160 detector configuration) and normal resolution mode (NR, 0.5 mm x 80 detector configuration). The HR data were reconstructed using 1024 x 1024 matrix (0.22 x 0.22 x 0.25 mm voxel size) and the NR data were reconstructed using a 512 x 512 matrix (0.43 x 0.43 x 0.50 mm). Two reconstruction algorithms (filtered back projection; FBP and an adaptive iterative dose reduction 3D algorithm; AIDR 3D) and three reconstruction kernels (FC30, FC52 and FC56) were investigated. The CTDI_vol dose values ranged from 0.2 to 6.2 mGy. A refined automated full-width half-maximum (FWHM) method was used for the measurement of airway dimensions, where the density profiles were computed by radial oversampling using a polar coordinate system. Both ID and WT were compared to the known dimensions using a regression model, and the root-mean-squared-error (RMSE) and average error were computed across all 14 airway tubes.

RESULTS

The results indicate that the ID can be measured within a 15% error down to approximately 0.8 mm and 2.0 mm using the HR and NR modes, respectively. The overall RMSE (and average error) of ID measurements for HR and NR were 0.10 mm (-0.70%) and 0.31 mm (-2.63%), respectively. The RMSE (and average error) of WT measurements using HR and NR were 0.10 mm (23.27%) and 0.27 mm (53.56%), respectively. The WT measurement using HR yielded a factor of two improvement in accuracy as compared to NR.

CONCLUSIONS

High-resolution CT can provide more accurate measurements of airway dimensions as compared with normal resolution CT, potentially improving quantitative assessment of pathologies such as COPD and asthma. The high resolution mode acquired and reconstructed with AIDR3D and the FC52 kernel provides most accurate measurement of airway dimensions. Low-dose high resolution measurements at dose level above 0.9 mGy can provide improved accuracy on both inner diameters and wall thicknesses compared to full dose normal resolution airway phantom measurements.

Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features

Purpose: A computer-aided diagnosis (CADx) system for breast masses is proposed, which incorporates both handcrafted and convolutional radiomic features embedded into a single deep learning model. Approach: The model combines handcrafted and convolutional radiomic signatures into a multi-view architecture, which retrieves three-dimensional (3D) image information by simultaneously processing multiple two-dimensional mass patches extracted along different planes through the 3D mass volume. Each patch is processed by a stream composed of two concatenated parallel branches: a multi-layer perceptron fed with automatically extracted handcrafted radiomic features, and a convolutional neural network, for which discriminant features are learned from the input patches. All streams are then concatenated together into a final architecture, where all network weights are shared and the learning occurs simultaneously for each stream and branch. The CADx system was developed and tested for diagnosis of breast masses ( N = 284 ) using image datasets acquired with independent dedicated breast computed tomography systems from two different institutions. The diagnostic classification performance of the CADx system was compared against other machine and deep learning architectures adopting handcrafted and convolutional approaches, and three board-certified breast radiologists. Results: On a test set of 82 masses (45 benign, 37 malignant), the proposed CADx system performed better than all other model architectures evaluated, with an increase in the area under the receiver operating characteristics curve (AUC) of 0.05 ± 0.02 , and achieving a final AUC of 0.947, outperforming the three radiologists ( AUC = 0.814 - 0.902 ). Conclusions: In conclusion, the system demonstrated its potential usefulness in breast cancer diagnosis by improving mass malignancy assessment.

Optical conductivity of triple point fermions

As a low-energy effective theory on non-symmorphic lattices, we consider a generic triple point fermion Hamiltonian, which is parameterized by an angular parameterλ. We find strongλdependence in both Drude and interband optical absorption of these systems. The deviation of theT2coefficient of the Drude weight from Dirac/Weyl fermions can be used as a quick way to optically distinguish the triple point degeneracies from the Dirac/Weyl degeneracies. At the particularλ=π/6 point, we find that the 'helicity' reversal optical transition matrix element is identically zero. Nevertheless, deviating from this point, the helicity reversal emerges as an absorption channel.

Total variation with modified group sparsity for CT reconstruction under low SNR

BACKGROUND AND OBJECTIVE

Since the stair artifacts may affect non-destructive testing (NDT) and diagnosis in the later stage, an applicable model is desperately needed, which can deal with the stair artifacts and preserve the edges. However, the classical total variation (TV) algorithm only considers the sparsity of the gradient transformed image. The objective of this study is to introduce and test a new method based on group sparsity to address the low signal-to-noise ratio (SNR) problem.

METHODS

This study proposes a weighted total variation with overlapping group sparsity model. This model combines the Gaussian kernel and overlapping group sparsity into TV model denoted as GOGS-TV, which considers the structure sparsity of the image to be reconstructed to deal with the stair artifacts. On one hand, TV is the accepted commercial algorithm, and it can work well in many situations. On the other hand, the Gaussian kernel can associate the points around each pixel. Quantitative assessments are implemented to verify this merit.

RESULTS

Numerical simulations are performed to validate the presented method, compared with the classical simultaneous algebraic reconstruction technique (SART) and the state-of-the-art TV algorithm. It confirms the significantly improved SNR of the reconstruction images both in suppressing the noise and preserving the edges using new GOGS-TV model.

CONCLUSIONS

The proposed GOGS-TV model demonstrates its advantages to reduce stair artifacts especially in low SNR reconstruction because this new model considers both the sparsity of the gradient image and the structured sparsity. Meanwhile, the Gaussian kernel is utilized as a weighted factor that can be adapted to the global distribution.

Sparse-view, short-scan, dedicated cone-beam breast computed tomography: image quality assessment

The purpose of this study is to quantify the impact of sparse-view acquisition in short-scan trajectories, compared to 360-degrees full-scan acquisition, on image quality measures in dedicated cone-beam breast computed tomography (BCT). Projection data from 30 full-scan (360-degrees; 300 views) BCT exams with calcified lesions were selected from an existing clinical research database. Feldkamp-Davis-Kress (FDK) reconstruction of the full-scan data served as the reference. Projection data corresponding to two short-scan trajectories, 204 and 270-degrees, which correspond to the minimum and maximum angular range achievable in a cone-beam BCT system were selected. Projection data were retrospectively sampled to provide 225, 180, and 168 views for 270-degrees short-scan, and 170 views for 204-degrees short-scan. Short-scans with 180 and 168 views in 270-degrees used non-uniform angular sampling. A fast, iterative, total variation-regularized, statistical reconstruction technique (FIRST) was used for short-scan image reconstruction. Image quality was quantified by variance, signal-difference to noise ratio (SDNR) between adipose and fibroglandular tissues, full-width at half-maximum (FWHM) of calcifications in two orthogonal directions, as well as, bias and root-mean-squared-error (RMSE) computed with respect to the reference full-scan FDK reconstruction. The median values of bias (8.6 × 10-4-10.3 × 10-4cm-1) and RMSE (6.8 × 10-6-9.8 × 10-6cm-1) in the short-scan reconstructions, computed with the full-scan FDK as the reference were close to, but not zero (P < 0.0001, one-sample median test). The FWHM of the calcifications in the short-scan reconstructions did not differ significantly from the reference FDK reconstruction (P > 0.118), except along the superior-inferior direction for the short-scan reconstruction with 168 views in 270-degrees (P = 0.046). The variance and SDNR from short-scan reconstructions were significantly improved compared to the full-scan FDK reconstruction (P < 0.0001). This study demonstrates the feasibility of the short-scan, sparse-view, compressed sensing-based iterative reconstruction. This study indicates that shorter scan times and reduced radiation dose without sacrificing image quality are potentially feasible.